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Continual Evolution Strategies in Control Tasks

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Computer Science > Neural and Evolutionary Computing

arXiv:2608.13600 (cs)
[Submitted on 3 Aug 2026]

Title:Continual Evolution Strategies in Control Tasks

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Abstract:We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones. On sequential MuJoCo locomotion tasks, naive ES suffers from severe catastrophic forgetting. Replay substantially improves retention and can induce positive transfer, while larger replay budgets reduce plasticity. Overall, these results show that ES can support continual adaptation in control and that replay is an effective mechanism for mitigating forgetting.
Comments: Accepted for publication in the GECCO 2026 Companion Proceedings
Subjects: Neural and Evolutionary Computing (cs.NE); Machine Learning (cs.LG)
Cite as: arXiv:2608.13600 [cs.NE]
  (or arXiv:2608.13600v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2608.13600
arXiv-issued DOI via DataCite

Submission history

From: Nicola Pitzalis [view email]
[v1] Mon, 3 Aug 2026 19:05:17 UTC (4,300 KB)
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